Local Composite Quantile Regression for Regression Discontinuity

We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs. <b>Kai <i>et al.</i></b> (<b>2010</b>) study the efficiency property of LCQR, while we show that its nice boundary performance translates to accurate estimation of treatment effects in RD under a variety of data generating processes. Moreover, we propose a bias-corrected and standard error-adjusted <i>t</i>-test for inference, which leads to confidence intervals with good coverage probabilities. A bandwidth selector is also discussed. For illustration, we conduct a simulation study and revisit a classic example from <b>Lee</b> (<b>2008</b>). A companion R package rdcqr is developed.